Method

A Uni fied Multi-scale Deep Convolutional Neural Network for Fast Object Detection [MS-CNN]
https://github.com/zhaoweicai/mscnn

Submitted on 14 Jul. 2016 06:41 by
Zhaowei Cai (UCSD)

Running time:0.4 s
Environment:GPU @ 2.5 Ghz (C/C++)

Method Description:
Please check the paper.
Parameters:
Please check the paper.
Latex Bibtex:
@inproceedings{Cai2016ECCV,
title = {A Unified Multi-scale Deep
Convolutional Neural Network for Fast Object
Detection},
author = {Zhaowei Cai and Quanfu Fan and
Rogerio Feris and Nuno Vasconcelos},
booktitle = {ECCV},
year = {2016}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 93.87 % 88.68 % 76.11 %
Pedestrian (Detection) 85.71 % 74.89 % 68.99 %
Cyclist (Detection) 84.88 % 75.30 % 65.27 %
This table as LaTeX

2D object detection results.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot




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